Demand response clustering - How do dynamic prices affect household electricity consumption?

Waczowicz, Simon; Reischl, Markus; Hagenmeyer, Veit; Mikut, Ralf; Klaiber, Stefan; Bretschneider, Peter; Konotop, Irina; Westermann, Dirk

doi:10.1109/ptc.2015.7232493

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…Power quality power quality disturbances classification ( [22], [33], [34], [37], [63], [73], [104], [121], [155], [170], [215]), power data compression ( [55], [59], [71], [133], [134], [140], [181], [192], [231], [244], [246]), meter placement for quality estimation ( [1], [9]), energy losses detection ( [38]), missing data imputation [177], [205] C7. Pricing pricing forecasting ( [5], [186], [188], [189], [200], [222]- [225], [229], [243], [249]), pricing impact on customer behaviour ( [27], [241]), pricing for demand-side management ( [91], [107])…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…[20] and histogram of distances [13], respectively, to choose hyperparameter settings without any further validation. Al-Otabi et al [7] and Waczowicz et al [9] specified their own CVI and used it to obtain their results. Whereas, Motlagh et al [1] not only worked with just one clustering approach but also heuristically opted for a fixed number of clusters.…”

Section: A Related Workmentioning

confidence: 99%

“…Instead of hard (or definite) labels, Fuzzy C-Means (FCM) clustering algorithm identifies fuzzy labels and is the last candidate algorithm that is explored in this work. Many recent studies about clustering residential electric demand profiles use this algorithm [5], [9], [12]. It associates each household (say, x) with all the clusters using a membership value (or the degree of association).…”

Section: B Unsupervised Clustering Algorithmsmentioning

confidence: 99%

Validating Clustering Frameworks for Electric Load Demand Profiles

Jain¹,

Sark²,

Dev³

2021

Preprint

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Large-scale deployment of smart meters has made it possible to collect sufficient and high-resolution data of residential electric demand profiles. Clustering analysis of these profiles is important to further analyze and comment on electricity consumption patterns. Although many clustering techniques have been proposed in the literature over the years, it is often noticed that different techniques fit best for different datasets.To identify the most suitable technique, standard clustering validity indices are often used. These indices focus primarily on the intrinsic characteristics of the clustering results. Moreover, different indices often give conflicting recommendations which can only be clarified with heuristics about the dataset and/or the expected cluster structures -information that is rarely available in practical situations. This paper presents a novel scheme to validate and compare the clustering results objectively. Additionally, the proposed scheme considers all the steps prior to the clustering algorithm, including the pre-processing and dimensionality reduction steps, in order to provide recommendations over the complete framework. Accordingly, the proposed strategy is shown to provide better, unbiased, and uniform recommendations as compared to the standard Clustering Validity Indices.

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